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Netlab Reference Manual pca
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<H1> pca
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<h2>
Purpose
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Principal Components Analysis

<p><h2>
Synopsis
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<PRE>
PCcoeff = pca(data)
PCcoeff = pca(data, N)
[PCcoeff, PCvec] = pca(data)
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<p><h2>
Description
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<CODE>PCcoeff = pca(data)</CODE> computes the eigenvalues of the covariance
matrix of the dataset <CODE>data</CODE> and returns them as <CODE>PCcoeff</CODE>.  These
coefficients give the variance of <CODE>data</CODE> along the corresponding 
principal components.  

<p><CODE>PCcoeff = pca(data, N)</CODE> returns the largest <CODE>N</CODE> eigenvalues.

<p><CODE>[PCcoeff, PCvec] = pca(data)</CODE> returns the principal components as
well as the coefficients.  This is considerably more computationally
demanding than just computing the eigenvalues.

<p><h2>
See Also
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<CODE><a href="eigdec.htm">eigdec</a></CODE>, <CODE><a href="gtminit.htm">gtminit</a></CODE>, <CODE><a href="ppca.htm">ppca</a></CODE><hr>
<b>Pages:</b>
<a href="index.htm">Index</a>
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<p>Copyright (c) Ian T Nabney (1996-9)


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